The International Conference on Harmonization (ICH) Q8(R2), Q9, and Q10 guidelines provide the foundation for implementing Quality by Design (QbD). Applying those concepts to the manufacture of biotech products, however, involves some nuances and complexities. Therefore, this paper offers guidance and interpretation for implementing QbD for biopharmaceuticals, from early-phase development steps such as identifying critical quality attributes and setting specifications, followed by the development of the design space and establishing the process control strategy; to later stages, including incorporating QbD into a regulatory filing and facilitating efficient commercial processes and manufacturing change flexibility post licensure.

(Eli Lilly and Company)

This paper focuses on the factors to consider when applying the Quality by Design (QbD) concepts outlined in ICH Q8(R2), Q9, and Q10 to biotechnology products. Although biologic and biotechnology products often present a higher level of complexity than small molecules in terms of manufacturing process or product structure, the concepts of QbD are the same as those for small molecules. This paper describes the nuances and complexities involved in implementing QbD in the manufacture of biotech products and offers guidance and interpretation for doing so. The scope of this paper is limited to well-characterized protein products, in which the natural molecular heterogeneity, impurity profile, and potency can be defined with a high degree of confidence.

Part 1 of this three-part article, which appeared in the November issue, covered molecular design, the use of laboratory and clinical studies to identify critical quality attributes, setting specifications, and developing the design space. Here, in Part 2, we address the use of design of experiments (DOE) to define the design space, unique considerations for process development for biopharmaceuticals, the establishment of a control strategy, and the placement of QbD information in a regulatory application.

USE OF DOE TO DEFINE THE DESIGN SPACE

Process parameters that have been identified, through risk analysis, as potentially having a significant impact on subsequent process steps, may be studied using multifactor DOE. DOE is more efficient and effective than traditional "one-factor-at-a-time" (OFAT) experiments. DOE is more efficient because it (1) requires fewer numbers of experimental runs, and (2) covers a broader "knowledge space" than OFAT experimentation. As a result, it is more effective in (1) investigating potential interactions among process factors, (2) avoiding artifacts such as experimental clustering and run order through randomization, and (3) making use of "hidden replication," and thus in having better sensitivity for detecting important effects.

Figure 1

The mathematical model that is derived from DOE can be used together with the acceptable boundaries of critical quality attributes (CQAs) to define a design space for a given process step. This is illustrated in Figure 1. Two response factors (X1 and X2) are studied across the knowledge space that has been defined by the multifactor DOE. These factors yield a response surface for a CQA (Panel 1). The response surface intersects the lower (Panel 2) and upper (Panel 3) specification limits (USL and LSL) for a subsequent process step, to yield its design space (Panel 4). That space represents the normal operating ranges for the factors, falling well within the design space (Panel 5). Operating within this normal operating range (NOR) will yield quality attribute measurements that fall within the upper and lower control limits (UCL and LCL in Panel 6). Because the LCL and UCL fall well within the LSL and USL, the process step is predicted to be highly capable of delivering product that meets the requirements of subsequent steps in the process. Excursions outside the NOR are expected to deliver product with quality attributes that are acceptable for further processing, as long as the operating parameters are held to limits defined by the design space.

Figure 1 illustrates the design space for a single quality attribute. In biotech processes, however, it is typical for a single unit operation to affect several quality attributes. For such cases, the design space for the unit operation is obtained from overlays of the design spaces derived from analyses of multiple attributes, or from a multivariate analysis of the system. In addition, the design space is derived from a mathematical model that is subject to uncertainty and to the limits of the model. With continual verification and updating of process information through information management, the uncertainty is reduced because more data are gathered and included during the lifecycle of a product.